SEMICONDUCTOR PHOTOCATALYSIS: MECHANISMS, PHOTOCATALYTIC PERFORMANCES AND LIFETIME OF REDOX CARRIERS

Photocatalytic reactions mediated by semiconductors such as ZnS, TiO2, ZnO, etc. can harvest solar energy into chemical bonds, a process with important prebiotic and environmental chemistry applications. The recycling of CO2 into organic molecules (e.g., formate, methane, and methanol) facilitated by irradiated semiconductors such as colloidal ZnS nanoparticles has been demonstrated. ZnS can also drive prebiotic reactions from the reductive tricarboxylic acid (rTCA) cycle such as the reduction of fumarate to succinate. However, the mechanism of photoreduction by ZnS of the previous reaction has not been understood. Thus, this thesis reports the mechanisms for heterogeneous photocatalytic reductions on ZnS for two model reactions in water with sulfide hole scavenger. First the reduction of CO2 is carried out under variable wavelength of irradiation and proposed to proceed thorough five steps resulting in the exclusive formation of formate. Second the reduction of the double bond of fumaric acid to succinic acid is reported in detail and compared to the previous conversion of CO2 to formic acid. Both reactions are carried out under variable wavelength of irradiation and proposed to proceed thorough one electron transfer at a time. In addition, a new method to measure the bandgap of colloidal ZnS suspended in water is established. Furthermore, the time scales of electron transfer and oxidizing hole loss during irradiation of ZnS for both reactions are reported and interpreted in terms of the Butler-Volmer equation. The sunlight promoted production of succinate introduced above, provides a connection of this prebiotic chemistry work to explore if central metabolites of the rTCA cycle can catalyze the synthesis of clay minerals. Clay minerals are strong adsorbents that can retain water and polar organic molecules, which facilitate the polymerization of biomolecules and conversion of fatty acid micelles into vesicles under prebiotic conditions relevant to the early Earth. While typical clay formation requires high temperatures and pressures, this process is hypothesized herein to be accelerated by central metabolites. A series of synthesis are designed to last only 20 hours to study the crystallization of sauconite, an Al- and Zn-rich model clay, at low temperature and ambient pressure in the presence of succinate as a catalyst. Succinate promotes the formation of the trioctahedral 2:1 layer silicate at ≥ 75 °C, 6.5 ≤ pH ≤ 14, [succinate] ≥ 0.01 M. Cryogenic and conventional transmission electron microscopies, X-ray diffraction, diffuse reflectance Fourier transformed infrared spectroscopy, and measurements of total surface area and cation exchange capacity are used to study the time evolution during the synthesis of sauconite. While the studies with ZnS presented above advanced the fundamental understanding of photocatalysis with single semiconductors, the environmental applications of this material appear limited. A common limitation to photocatalysis with single semiconductors is the rapid recombination of photogenerated electron-hole pairs, which reduces significantly the efficiency of the process that in the case of ZnS also suffers from photocorrosion in the presence of air. In order to overcome the fast charge recombination and the limited visible-light absorption of semiconductor photocatalysts, an effective strategy is developed in this work by combining two semiconductors into a nanocomposite. This nanocomposite is solvothermally synthesized creating octahedral cuprous oxide covered with titanium dioxide nanoparticles (Cu2O/TiO2). The nanocomposite exhibits unique surface modifications that provide a heterojunction with a direct Z-scheme for optimal CO2 reduction. The band structure of the nanocomposite is characterized by diffused reflectance UV-visible spectroscopy, X-ray photoelectron spectroscopy (XPS) and ultraviolet photoelectron spectroscopy. The photoreduction of CO2(g) to CO(g) on the nanocomposite is investigated in the presence water vapor as the hole scavenger that generates the quantifiable hydroxyl radical (). The quantum efficiency of CO production under irradiation at λ ≥ 305 nm with the nanocomposite is 2-times larger than for pure Cu2O. The detection of and XPS analysis contrasting the stability of Cu2O/TiO2 vs Cu2O during irradiation prove that TiO2 prevents the photocorrosion of Cu2O. Overall, the studies of photocatalytic reductions on single component semiconductors reveal new knowledge needed for developing future photocatalytic application for fuel production, wastewater treatment, reducing air pollution, and driving important prebiotic chemistry reactions. Furthermore, the design of a photocatalyst operating under a Z-scheme mechanism provides a new proof of concept for the design of systems that mimic photosynthesis. Finally, this work also demonstrates how molecules obtained by mineral mediated photochemistry can catalyze clay formation; highlighting the important role that photochemistry may have played for the origin of life on the early Earth and other rocky planets

CO\u3csub\u3e2\u3c/sub\u3e Reduction under Periodic Illumination of ZnS

The photoreduction of CO2 to formate (HCOO–) in sphalerite (ZnS) aqueous suspensions is systematically studied in the presence of Na2S hole scavenger. A series of cut-on filters at λcut-on ≥ 280, 295, 305, 320, and 400 nm are used to measure the reaction rate of formate production. The dependence of the measured reaction rates on λcut-on indicates that a wavelength of λ = 345 nm is associated with the actual bandgap of the semiconductor nanocrystallites suspended in water. The results from apparent quantum yield measurements during periodic illumination experiments suggest that (1) valence-band holes on the surface of ZnS disappear within deciseconds due to the oxidation of the scavenger while simultaneously pumping electrons to the conduction band, (2) excited electrons in the conduction band of ZnS are transferred to CO2 to produce the intermediate CO2•–, and (3) CO2•– abstracts a proton from water and undergoes further photoreduction on the surface of ZnS in an overall time scale for steps 2 + 3 of a few milliseconds. The separation of both process merges at ∼29 ms because it decreases exponentially with a drop in [Na2S] accompanied by a less negative surface potential. The behavior of the reaction rate at variable pH resembles the fraction of dissolved CO2, discarding the direct participation of bicarbonate and carbonate in the reaction. Combined chromatographic, mass spectrometry, and spectroscopic studies provide new insights to understand the role of surface chemistry on the photoreduction of CO2 on ZnS nanocrystals

Numerical Simulation on Heat Transfer Performance of Silicon Carbide/ Nitrate Composite for Solar Power Generation

KNO3 was used as the phase change material (PCM), but its thermal conductivity is too low to transfer heat between the PCM and conduction oil efficiently. In this thesis, on the basis of the previous studies (Yong Li, 2015), the solar power generation efficiency is enhanced with high temperature interval (280℃—400℃), and the new composite which are composed by the SiC honeycomb (SCH) frame and infiltrated KNO3 is simulated by using Fluent software. The results show that the new composite of the KNO3 +30%SCH suit for the requirement of the charging time and capacity in the design of the thermal energy storage units (TESU); The comparable simulation for the long and short pipe models supplies the evidences that the long pipe simulation can be substituted by the short pipe simulation relatively, which reduces the 3-D simulation time enormously; The comparable simulation of the radial dimensions supplies some theory foundations for the design of the module thermal energy storage tank (MTEST) . These simulation results have important guidance on the design of the thermal energy storage unit and the module thermal energy storage tank

Enhanced Derivation of Customer-Specific Drive System Design Parameters with Time Frame-Based Maximum Load Analysis

Only a small part of the high performance of electric drive systems in vehicles is used in everyday operation by customers. As a result, most drives are not operated in the optimum efficiency range. Designing a suitable drive system, whose performance is aligned with actual customer requirements, presents the potential to increase efficiency. Based on the findings of previous research, this paper serves to complement an existing method, which already introduced the basic method of transferring statistical customer data into relevant parameters for the design of a customer-specific drive system. In order to improve the method, further criteria for the selection of relevant time series come into place. Furthermore, the impact on maximum loads resulting from various sequences of the selected time series is identified and evaluated with time frame-based analysis. A new approach for the effective computation of maximum design-relevant loads in the admissible time frame range is introduced and validated. By taking this approach, the sensitivity of the derived design parameters regarding various time series sequence is evaluated in the context of selected datasets. In addition, concatenations of time series are identified which may have a relevant influence on the maximum loads. Consequently, the design process is safeguarded thoroughly against potential maximum loads as well as the associated thermal stresses

Multi-target QSAR modelling in the analysis and design of HIV-HCV co-inhibitors: an in-silico study

<p>Abstract</p> <p>Background</p> <p>HIV and HCV infections have become the leading global public-health threats. Even more remarkable, HIV-HCV co-infection is rapidly emerging as a major cause of morbidity and mortality throughout the world, due to the common rapid mutation characteristics of the two viruses as well as their similar complex influence to immunology system. Although considerable progresses have been made on the study of the infection of HIV and HCV respectively, few researches have been conducted on the investigation of the molecular mechanism of their co-infection and designing of the multi-target co-inhibitors for the two viruses simultaneously.</p> <p>Results</p> <p>In our study, a multi-target Quantitative Structure-Activity Relationship (QSAR) study of the inhibitors for HIV-HCV co-infection were addressed with an in-silico machine learning technique, i.e. multi-task learning, to help to guide the co-inhibitor design. Firstly, an integrated dataset with 3 HIV inhibitor subsets targeted on protease, integrase and reverse transcriptase respectively, together with another 6 subsets of 2 HCV inhibitors targeted on NS3 serine protease and NS5B polymerase respectively were compiled. Secondly, an efficient multi-target QSAR modelling of HIV-HCV co-inhibitors was performed by applying an accelerated gradient method based multi-task learning on the whole 9 datasets. Furthermore, by solving the <it>L</it>-1-infinity regularized optimization, the Drug-like index features for compound description were ranked according to their joint importance in multi-target QSAR modelling of HIV and HCV. Finally, a drug structure-activity simulation for investigating the relationships between compound structures and binding affinities was presented based on our multiple target analysis, which is then providing several novel clues for the design of multi-target HIV-HCV co-inhibitors with increasing likelihood of successful therapies on HIV, HCV and HIV-HCV co-infection.</p> <p>Conclusions</p> <p>The framework presented in our study provided an efficient way to identify and design inhibitors that simultaneously and selectively bind to multiple targets from multiple viruses with high affinity, and will definitely shed new lights on the future work of inhibitor synthesis for multi-target HIV, HCV, and HIV-HCV co-infection treatments.</p

High-Throughput Screening of Transition Metal Single-Atom Catalysts for Nitrogen Reduction Reaction

The discovery of metals as catalytic centers for nitrogen reduction reactions has stimulated great enthusiasm for single-atom catalysts. However, the poor activity and low selectivity of available SACs are far away from the industrial requirement. Through the high throughout first principles calculations, the doping engineering can effectively regulate the NRR performance of b-Sb monolayer. Especially, the origin of activated N2 is revealed from the perspective of the electronic structure of the active center. Among the 24 transition metal dopants, Re@Sb and Tc@Sb showed the best NRR catalytic performance with a low limiting potential. The Re@Sb and Tc@Sb also could significantly inhibit HER and achieve a high theoretical Faradaic efficiency of 100%. Our findings not only accelerate discovery of catalysts for ammonia synthesis but also contribute to further elucidate the structure-performance correlations

A Numerical Study on the Temperature Field of a R290 Hermetic Reciprocating Compressor with Experimental Validation

A numerical model to predict the temperature field in a R290 hermetic reciprocating compressor is presented in this work. The control volume method and the lumped parameter method are used in the simulation. The compressor is divided into 6 control volumes, including the suction muffler, the cylinder, the discharge chamber, the discharge muffler, the discharge pipe and the shell. The system of non-linear equations is formed of the energy balance equations of every control column. The temperature field is derived by solving the equations. To valid the numerical model accurately, temperature experiment has been carried out in 3 same-type hermetic reciprocating compressors using R290 as working fluid. The simulation result shows a good agreement compared with the experiment

Training Socially Aligned Language Models on Simulated Social Interactions

Social alignment in AI systems aims to ensure that these models behave according to established societal values. However, unlike humans, who derive consensus on value judgments through social interaction, current language models (LMs) are trained to rigidly replicate their training corpus in isolation, leading to subpar generalization in unfamiliar scenarios and vulnerability to adversarial attacks. This work presents a novel training paradigm that permits LMs to learn from simulated social interactions. In comparison to existing methodologies, our approach is considerably more scalable and efficient, demonstrating superior performance in alignment benchmarks and human evaluations. This paradigm shift in the training of LMs brings us a step closer to developing AI systems that can robustly and accurately reflect societal norms and values.Comment: Code, data, and models can be downloaded via https://github.com/agi-templar/Stable-Alignmen

High-Dose siRNAs Upregulate Mouse Eri-1 at both Transcription and Posttranscription Levels

The eri-1 gene encodes a 3′ exonuclease that can negatively regulate RNA interference via siRNase activity. High-dose siRNAs (hd-siRNAs) can enhance Eri-1 expression, which in return degrade siRNAs and greatly reduces RNAi efficiency. Here we report that hd-siRNAs induce mouse Eri-1 (meri-1) expression through the recruitment of Sp1, Ets-1, and STAT3 to the meri-1 promoter and the formation of an Sp1-Ets-1-STAT3 complex. In addition, hd-siRNAs also abolish the 3′ untranslated region (UTR) mediated posttranscriptional repression of meri-1. Our findings demonstrate the molecular mechanism underlying the upregulation of meri-1 by hd-siRNA